Evaluating different machine learning methods to simulate runoff from extensive green roofs

Green roofs are increasingly popular measures to permanently reduce or delay storm-water runoff. The main objective of the study was to examine the potential of using machine learning (ML) to simulate runoff from green roofs to estimate their hydrological performance. Four machine learning methods, artificial neural network (ANN), M5 model tree, long short-term memory (LSTM) and k nearest neighbour (kNN), were applied to simulate storm-water runoff from 16 extensive green roofs located in four Norwegian cities across different climatic zones. The potential of these ML methods for estimating green roof retention was assessed by comparing their simulations with a proven conceptual retention model. Furthermore, the transferability of ML models between the different green roofs in the study was tested to investigate the potential of using ML models as a tool for planning and design purposes. The ML models yielded low volumetric errors that were comparable with the conceptual retention models, which indicates good performance in estimating annual retention. The ML models yielded satisfactory modelling results (NSE >0.5) in most of the roofs, which indicates an ability to estimate green roof detention. The variations in ML models' performance between the cities was larger than between the different configurations, which was attributed to the different climatic characteristics between the four cities. Transferred ML models between cities with similar rainfall events characteristics (Bergen–Sandnes, Trondheim–Oslo) could yield satisfactory modelling performance (Nash–Sutcliffe efficiency NSE >0.5 and percentage bias |PBIAS| <25 %) in most cases. However, we recommend the use of the conceptual retention model over the transferred ML models, to estimate the retention of new green roofs, as it gives more accurate volume estimates. Follow-up studies are needed to explore the potential of ML models in estimating detention from higher temporal resolution datasets

Constraints on shrub cover and shrub-shrub competition in a U.S. southwest desert

The cover of woody perennial plants (trees and shrubs) in arid ecosystems is at least partially constrained by water availability. However, the extent to which maximum canopy cover is limited by rainfall and the degree to which soil water holding capacity and topography impacts maximum shrub cover are not well understood. Similar to other deserts in the U.S. southwest, plant communities at the Jomada Basin Long-Term Ecological Research site in the northern Chihuahuan Desert have experienced a long-term state change from perennial grassland to shrubland dominated by woody plants. To better understand this transformation, and the environmental controls and constraints on shrub cover, we created a shrub cover map using high spatial resolution images and explored how maximum shrub cover varies with landform, water availability, and soil characteristics. Our results indicate that when clay content is below similar to 18%, the upper limit of shrub cover is positively correlated with plant available water as mediated by surface soil clay influence on water retention. At surface soil day contents >18%, maximum shrub cover decreases, presumably because the amount of water percolating to depths preferentially used by deep-rooted shrubs is diminished. In addition, the relationship between shrub cover and density suggests that self-thinning occurs in denser stands in most landforms of the Jomada Basin, indicating that shrub-shrub competition interacts with soil properties to constrain maximum shrub cover in the northern Chihuahuan Desert.National Science Foundation [1235828]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield

Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what level of priority should stakeholders commit to in order to obtain these data? Before fully addressing these questions a remaining challenge is the complex nature of spatiotemporal yield variation. Here, a methodological framework is presented to separate the spatial and temporal components of crop yield variation at the subfield level. The framework can also be used to quantify the benefits of different data types on the predicted crop yield as well to better understand the connection of that data to underlying mechanisms controlling yield. Here, fine-resolution (10 m) datasets were assembled for eight 64 ha field sites, spanning a range of climatic, topographic, and soil conditions across Nebraska. Using Empirical Orthogonal Function (EOF) analysis, we found the first axis of variation contained 60–85 % of the explained variance from any particular field, thus greatly reducing the dimensionality of the problem. Using Multiple Linear Regression (MLR) and Random Forest (RF) approaches, we quantified that location within the field had the largest relative importance for modeling crop yield patterns. Secondary factors included a combination of vegetation condition, soil water content, and topography. With respect to predicting spatiotemporal crop yield patterns, we found the RF approach (prediction RMSE of 0.2−0.4 Mg/ha for maize) was superior to MLR (0.3−0.8 Mg/ha). While not directly comparable to MLR and RF the EOF approach had relatively low error (0.5–1.7 Mg/ha) and is intriguing as it requires few calibration parameters (2–6 used here) and utilizes the climate-based aridity index, allowing for pragmatic long-term predictions of subfield crop yield

Haloarchaeal communities in the crystallizers of two adriatic solar salterns

Abstract: Solar salterns operate only for short dry periods of the year in the north shore of the Adriatic Sea because of its relatively humid and cold Mediterranean climate. In a previous paper, we showed that the NaCl precipitation ponds (crystallizers) of Northern Adriatic Seovlje salterns have different haloarchaeal populations from those typically found in dry and hot climates such as Southern Spain. To check whether there is a common pattern of haloarchaeal diversity in these less extreme conditions, diversity in crystallizers of other Adriatic solar salterns in Ston, Croatia was ascertained by molecular and culture methods. In addition, the cultivation approach was used to further describe haloarchaeal diversity in both salterns. Over the period of two solar salt collection seasons, isolates related to species of the genera Haloferax, Haloarcula, and Haloterrigena were recovered from both salterns. Within the same sampling effort, relatives of the genus Halorubrum and a Natrinema-like isolate were cultivated from Slovenian Seovlje salterns while Halobacterium related isolates were obtained from the Croatian Ston salterns. Concurrent with our previous findings, a library of Croatian saltern crystallizer PCR-amplified 16S rRNA genes was dominated by sequences related to the genus Halorubrum. The microbial community structure was similar in both salterns but diversity indices showed greater values in Slovenian salterns when compared with Croatian salterns. Key words: 16S rRNA, Haloarchaea, saltern, hypersaline, halophiles. Résumé : Les salines solaires ne fonctionnent que pendant de brèves périodes de temps sec dans l&apos;année sur la rive nord de la mer Adriatique à cause de son climat méditerranéen relativement humide et froid. Dans un article précédent, nous avons démontré que les bassins de précipitation de NaCl (cristalliseurs) des salines de Seovlje de l&apos;Adriatique Nord renferment des populations haloarchéenes différentes de celles normalement retrouvées dans des climats chauds et secs tels que le sud de l&apos;Espagne. Pour vérifier s&apos;il existe une configuration commune à la diversité haloarchéene dans ces conditions moins extrêmes, nous avons décrit la diversité dans les cristalliseurs d&apos;autres salines solaires adriatiquesles salines Ston en Croatie -par des méthodes moléculaires et de culture. De plus, l&apos;approche de culture fut employée afin de décrire davantage la diversité haloarchéene des deux salines. Sur une période de deux saisons de collecte de sel solaire, des isolats apparentés à des espèces des genres Haloferax, Haloarcula et Haloterrigena ont été recueillis des deux salines. À l&apos;issue d&apos;un même échantillonnage, des parents du genre Halorubrum et un isolat semblable à Natrinema ont été cultivés à partir de la saline slovène de Seovlje alors que des isolats apparentés à Halobacterium ont été obtenus des salines Croates de Ston. En conformité avec nos découvertes précédentes, la banque de gènes de ARNr 16S amplifiés par PCR du cristalliseur salin Croate était dominée par des séquences apparentées au genre Halorubrum. La structure de la communauté microbienne était semblable dans les deux salines mais les indices de diversité ont démontré des valeurs supérieures dans les salines slovènes comparativement aux salines croates

How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: • A Bayesian model assessment which compares two sets of values using nested sampling. This approach is new in NF grouping. • A Arsinh-Ordered Weighted Average model (Arsinh-OWA) which applies the arsinh transformation to the distance between two NFs, and then rescales the result to the arsinh of a biologically relevant threshold before grouping using OWA based distance. This approach is new in NF grouping. • An x-fold comparison as used in the ECETOC NanoApp. • Euclidean distance, which is a highly established distance metric. The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods

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